Prediction model of BOF end-point phosphorus content and sulfur content based on LWOA-TSVR
Chunyang Shi, Shiyu Guo, Baoshuai Wang, Zhicai Ma, Chun lei Wu, Peng Sun
Abstract
Precise control of the end-point phosphorus and sulfur content in converter steelmaking is critical to ensuring steel quality. An end-point prediction model based on LWOA-TSVR is established to better control the BOF end-point content of phosphorus and sulfur. The prediction impact is compared to the models BP, SVM, and TSVR. The results indicate that the LWOA-TSVR model outperforms the other three models in terms of accuracy. And the prediction model is applied to a steel mill. The results showed that the hit rates of phosphorus content and sulfur content were: 96.3%, 81.7%, and 94.8%, 76.9% in the range of ±0.005% and ±0.003%, respectively. The double hit rate was: 87.63% in the range of ±0.005%. Thus, it is demonstrated that the LWOA-TSVR prediction model performs effective prediction of end-point phosphorus and sulfur content with prediction accuracy that exceeds that required by the real steelmaking process in a steel mill.